All methods are empirical in nature and have been developed over the last 20 years in the research group of J.Gasteiger.

The following chemical effects can be quantified:

• heats of formation
• bond dissociation energies
• sigma charge distribution
• pi charge distribution
• inductive effect
• resonance effect and delocalization energies
• polarizability effect

"); close_paragraph(); print_title("Applications", $font2, 2, 0); open_paragraph(); print(" PETRA has shown to be applicable in the following fields

• pharmaceutical lead discovery and lead optimization
• definition of similarity and diversity of combinatorial libraries
• quantitative structure activity and property relationships (QSAR and QSPR)
• spectra prediction and structure elucidation
• prediction of chemical reactivity

"); close_paragraph(); print_title("Online-Access", $font2, 2, 0); open_paragraph(); print("

Read a short introduction or go directly to the input forms (use buttons above):

It is possible to run the calculations for a molecule drawn by an interactive molecule editor
   or
to get all properties calculated by PETRA for datasets of up to $uploadLimit molecules via upload of a datafile "); close_paragraph(); print_title("Availability", $font2, 2, 0); open_paragraph(); print(" PETRA is available for various Unix derivatives:

• IRIX5.3 to 6.5
• SunOS5.6 and lower
• HP-UX10.10
• Linux (Kernel2.2/2.4/glibc2 and higher)

Please contact info@mol-net.de at Molecular Networks GmbH if you are interested in evaluation of the program and our web interface is not sufficient for your purpose. "); close_paragraph(); /* print_title("Development", $font2, 2, 0); open_paragraph(); print(" The current work focusses on the development of a QSPR model for the prediction of pK_a values that will be available within the next version 3.1 to be released end of summer 2001.
The approach we will integrate in PETRA bases on the quantification of the physicochemical effects that have an influence on the pK_a and the derivation of a quantitative model using a feed forward neural network with a back-propagation learning algorithm.
In an initial model we have shown the high predictive power of this approach as presented at the \"New Approaches in Drug Design and Discovery\"-Workshop, Rauischholzhausen March 2001. "); close_paragraph(); */ print_title("Publications", $font2, 2, 0); open_paragraph(); print(" J. Gasteiger, \"Empirical Methods for the Calculation of Physicochemical Data of Organic Compounds\",
in: \"Physical Property Prediction in Organic Chemistry\",
Ed. C. Jochum, M. G. Hicks, J. Sunkel, Springer Verlag, Heidelberg, 119-138, 1988

See a comprehensive list of publications about the calculation of

• electronic effects
• physicochemical data

in organic compounds. "); close_paragraph(); print_title("Manual", $font2, 2, 0); open_paragraph(); print(" The manual is online available in HTML,
or downloadable as PDF-File. "); close_paragraph(); /* print_title("Slide Show", $font2, 2, 0); open_paragraph(); close_paragraph(); */ print_title("Contact", $font2, 2, 0); open_paragraph(); print("

• For scientific questions and/or discussions concerning PETRA please contact Prof. Johann Gasteiger or Thomas Kleinöder
• If you want to evaluate or purchase an inhouse version of PETRA or use the data commerially, then please contact Molecular Networks GmbH.

"); close_paragraph(); print_footer($lastChange, $petraHomeUrl, $counterFile, "home"); close_document();